1
Delta Higher Institute for Engineering & Technology, Department of Electronics and Communications Engineering, Egypt.
(lobna.aziz@dhiet.edu.eg)
Abstract :
Industrial robots have made it possible for manufacturers to make elevated low-cost products, which are thus major elements of advanced production technologies. Welding, cleaning, assembling, dismantling, slotting for computer chips, labeling requirements, stacking pallets, quality inspection, and monitoring are just a few of the applications for robotic systems. All the features are completed with a high level of endurance, speed, and accuracy. Multiple and competing criteria must be assessed simultaneously in a comprehensive selection analysis to identify the effectiveness of robots. To provide an automated machine for such arc machining operation, simple multi-criteria decision-making (MCDM) technique based on the COPRAS method is described in this work. The COPRAS method calculates significance weights using objective preferences and ranks the options. The COPRAS technique was used to determine the ranking order. The findings revealed that MCDM techniques for robot selection are extremely useful. The study's peculiarity is that it uses COPRAS MCDM approaches to select industrial arc welding robots.
Keywords :
MCDM; Robot; Industrial; COPRAS
References :
[1] J. F. Engelberger, Robotics in practice: management and applications of industrial robots. Springer Science & Business Media, 2012.
[2] J. N. Pires, A. Loureiro, and G. Bölmsjo, Welding robots: technology, system issues and application. Springer Science & Business Media, 2006.
[3] V. Kumar, S. K. Albert, and N. Chanderasekhar, “Development of programmable system on chip-based weld monitoring system for quality analysis of arc welding process,” International Journal of Computer Integrated Manufacturing, vol. 33, no. 9, pp. 925–935, 2020.
[4] V. Chodha, R. Dubey, R. Kumar, S. Singh, and S. Kaur, “Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques,” Materials Today: Proceedings, vol. 50, pp. 709–715, 2022.
[5] H. K. Banga, P. Kalra, R. Kumar, S. Singh, and C. I. Pruncu, “Optimization of the cycle time of robotics resistance spot welding for automotive applications,” Journal of Advanced Manufacturing and Processing, vol. 3, no. 3, p. e10084, 2021.
[6] P. Kah, M. Shrestha, E. Hiltunen, and J. Martikainen, “Robotic arc welding sensors and programming in industrial applications,” International Journal of Mechanical and Materials Engineering, vol. 10, no. 1, pp. 1–16, 2015.
[7] P. E. Spector, S. Fox, L. M. Penney, K. Bruursema, A. Goh, and S. Kessler, “The dimensionality of counterproductivity: Are all counterproductive behaviors created equal?,” Journal of vocational behavior, vol. 68, no. 3, pp. 446–460, 2006.
[8] I. Karabegović and R. Mirza, “Automation of the welding process by use of industrial robots,” in International Conference “New Technologies, Development and Applications,” 2018, pp. 3–17.
[9] P. Chatterjee, V. M. Athawale, and S. Chakraborty, “Selection of industrial robots using compromise ranking and outranking methods,” Robotics and Computer-Integrated Manufacturing, vol. 26, no. 5, pp. 483–489, 2010.
[10] R. Kumar and R. K. Garg, “Optimal selection of robots by using distance based approach method,” Robotics and Computer-Integrated Manufacturing, vol. 26, no. 5, pp. 500–506, 2010.
[11] D. E. Koulouriotis and M. K. Ketipi, “A fuzzy digraph method for robot evaluation and selection,” Expert Systems with Applications, vol. 38, no. 9, pp. 11901–11910, 2011.
[12] E. E. Karsak, Z. Sener, and M. Dursun, “Robot selection using a fuzzy regression-based decision-making approach,” International Journal of Production Research, vol. 50, no. 23, pp. 6826–6834, 2012.
[13] S. Datta, N. Sahu, and S. Mahapatra, “Robot selection based on grey‐MULTIMOORA approach,” Grey Systems: Theory and Application, 2013.
[14] A. U. Rehman and A. Al-Ahmari, “Assessment of alternative industrial robots using AHP and TOPSIS,” International Journal of Industrial and Systems Engineering, vol. 15, no. 4, pp. 475–489, 2013.
[15] H.-C. Liu, M.-L. Ren, J. Wu, and Q.-L. Lin, “An interval 2-tuple linguistic MCDM method for robot evaluation and selection,” International Journal of Production Research, vol. 52, no. 10, pp. 2867–2880, 2014.
[16] R. Parameshwaran, S. P. Kumar, and K. Saravanakumar, “An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria,” Applied Soft Computing, vol. 26, pp. 31–41, 2015.
[17] D. K. Sen, S. Datta, S. K. Patel, and S. S. Mahapatra, “Multi-criteria decision making towards selection of industrial robot: Exploration of PROMETHEE II method,” Benchmarking: An International Journal, 2015.
[18] A. Khandekar and S. Chakraborty, “Selection of industrial robot using axiomatic design principles in fuzzy environment,” Decision Science Letters, vol. 4, no. 2, pp. 181–192, 2015.
[19] P. Karande, E. Zavadskas, and S. Chakraborty, “A study on the ranking performance of some MCDM methods for industrial robot selection problems,” International Journal of Industrial Engineering Computations, vol. 7, no. 3, pp. 399–422, 2016.
[20] R. E. Breaz, O. Bologa, and S. G. Racz, “Selecting industrial robots for milling applications using AHP,” Procedia computer science, vol. 122, pp. 346–353, 2017.
[21] M. Mathew, S. Sahu, and A. K. Upadhyay, “Effect of normalization techniques in robot selection using weighted aggregated sum product assessment,” Int. J. Innov. Res. Adv. Stud, vol. 4, no. 2, pp. 59–63, 2017.
[22] M. Simion, L. Socaciu, O. Giurgiu, and S. M. PETRIŞOR, “The selection of industrial robots for military industry using AHP method: a case study,” Acta Technica Napocensis-Series: Applied Mathematics, Mechanics, and Engineering, vol. 61, no. 2, 2018.
[23] Y.-X. Xue, J.-X. You, X. Zhao, and H.-C. Liu, “An integrated linguistic MCDM approach for robot evaluation and selection with incomplete weight information,” International Journal of Production Research, vol. 54, no. 18, pp. 5452–5467, 2016.
[24] F. Zhou, X. Wang, and M. Goh, “Fuzzy extended VIKOR-based mobile robot selection model for hospital pharmacy,” International Journal of Advanced Robotic Systems, vol. 15, no. 4, p. 1729881418787315, 2018.
[25] M. K. Ghorabaee, “Developing an MCDM method for robot selection with interval type-2 fuzzy sets,” Robotics and Computer-Integrated Manufacturing, vol. 37, pp. 221–232, 2016.
[26] K. Muduli, J. Pumwa, D. K. Yadav, R. Kumar, and S. Tripathy, “A Grey Relation Approach for Selection of Industrial Robot,” in 2018 International Conference on Information Technology (ICIT), 2018, pp. 141–144.
[27] J.-J. Wang, Z.-H. Miao, F.-B. Cui, and H.-C. Liu, “Robot evaluation and selection with entropy-based combination weighting and cloud TODIM approach,” Entropy, vol. 20, no. 5, p. 349, 2018.
[28] Y. Fu, M. Li, H. Luo, and G. Q. Huang, “Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making,” Robotics and Autonomous Systems, vol. 122, p. 103304, 2019.
[29] R. K. A. Bhalaji, S. Bathrinath, S. G. Ponnambalam, and S. Saravanasankar, “Analyze the factors influencing human-robot interaction using MCDM method,” Materials Today: Proceedings, vol. 39, pp. 100–104, 2021.
[30] R. Kumar, P. S. Bilga, and S. Singh, “Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation,” Journal of cleaner production, vol. 164, pp. 45–57, 2017.
[31] R. Kumar et al., “Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review,” Journal of materials research and technology, vol. 10, pp. 1471–1492, 2021.
[32] R. Kumar and S. Singh, “Selection of vacuum cleaner with Technique for Order Preference by Similarity to Ideal Solution method based upon multi-criteriadecision-making theory,” Measurement and Control, vol. 53, no. 3–4, pp. 627–634, 2020.
[33] G. Singh, S. Singh, C. Prakash, R. Kumar, R. Kumar, and S. Ramakrishna, “Characterization of three‐dimensional printed thermal‐stimulus polylactic acid‐hydroxyapatite‐based shape memory scaffolds,” Polymer Composites, vol. 41, no. 9, pp. 3871–3891, 2020.
[34] R. Kumar, A. Bhattacherjee, A. D. Singh, S. Singh, and C. I. Pruncu, “Selection of portable hard disk drive based upon weighted aggregated sum product assessment method: A case of Indian market,” Measurement and Control, vol. 53, no. 7–8, pp. 1218–1230, 2020.
[35] R. Kumar, P. S. Bilga, and S. Singh, “An investigation of energy efficiency in finish turning of EN 353 alloy steel,” Procedia CIRP, vol. 98, pp. 654–659, 2021.
[36] S. Narayanamoorthy, S. Geetha, R. Rakkiyappan, and Y. H. Joo, “Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection,” Expert Systems with Applications, vol. 121, pp. 28–37, 2019.
[37] T. Rashid, A. Ali, and Y.-M. Chu, “Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection,” Plos one, vol. 16, no. 2, p. e0246738, 2021.
[38] M. Nasrollahi, J. Ramezani, and M. Sadraei, “A FBWM-PROMETHEE approach for industrial robot selection,” Heliyon, vol. 6, no. 5, p. e03859, 2020.
[39] E. K. Zavadskas, A. Kaklauskas, and V. Sarka, “The new method of multicriteria complex proportional assessment of projects,” Technological and economic development of economy, vol. 1, no. 3, pp. 131–139, 1994.
| Style | # |
|---|---|
| MLA | Lobna Osman. "Multi-criteria Decision Making Model for Industrial Arc Welding Robot." International Journal of Wireless and Ad Hoc Communication, Vol. 4, No. 1, 2022 ,PP. 19-30 (Doi : https://doi.org/10.54216/IJWAC.040102) |
| APA | Lobna Osman. (2022). Multi-criteria Decision Making Model for Industrial Arc Welding Robot. Journal of International Journal of Wireless and Ad Hoc Communication, 4 ( 1 ), 19-30 (Doi : https://doi.org/10.54216/IJWAC.040102) |
| Chicago | Lobna Osman. "Multi-criteria Decision Making Model for Industrial Arc Welding Robot." Journal of International Journal of Wireless and Ad Hoc Communication, 4 no. 1 (2022): 19-30 (Doi : https://doi.org/10.54216/IJWAC.040102) |
| Harvard | Lobna Osman. (2022). Multi-criteria Decision Making Model for Industrial Arc Welding Robot. Journal of International Journal of Wireless and Ad Hoc Communication, 4 ( 1 ), 19-30 (Doi : https://doi.org/10.54216/IJWAC.040102) |
| Vancouver | Lobna Osman. Multi-criteria Decision Making Model for Industrial Arc Welding Robot. Journal of International Journal of Wireless and Ad Hoc Communication, (2022); 4 ( 1 ): 19-30 (Doi : https://doi.org/10.54216/IJWAC.040102) |
| IEEE | Lobna Osman, Multi-criteria Decision Making Model for Industrial Arc Welding Robot, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 4 , No. 1 , (2022) : 19-30 (Doi : https://doi.org/10.54216/IJWAC.040102) |